Tutorial Analisis Independent Sample T Test Menggunakan R studio lengkap dengan Sintaxnya

 Tutorial Analisis Independent Sample T Test Menggunakan R studio lengkap dengan Sintaxnya

LAGI BUTUH JASA ANALISIS DATA TERPERCAYA? WhatsApp Kami 

Step 1: Install and Load Required Packages

If you haven't already installed the necessary packages, you can do so by running:

R
install.packages("dplyr")   # For data manipulation

Load the packages:

R
library(dplyr)

Step 2: Create or Load Your Data

Assuming you already have your data, let's create a fictional example:

R
set.seed(123)  # Setting seed for reproducibility

# Creating two independent samples
group1 <- rnorm(30, mean = 50, sd = 10)
group2 <- rnorm(30, mean = 55, sd = 10)

# Combine the data into a data frame
data <- data.frame(
  Group = rep(c("Group1", "Group2"), each = 30),
  Value = c(group1, group2)
)

Step 3: Explore Your Data (Optional)

It's always a good idea to inspect your data before performing any statistical analysis. Use summary() and boxplot() to get a sense of the data distribution:

R
summary(data)
boxplot(Value ~ Group, data = data)

Step 4: Conduct the Independent Samples t-test

Now, perform the independent samples t-test using the t.test() function:

R
result <- t.test(Value ~ Group, data = data)

# Display the result
print(result)

Step 5: Interpret the Results

The t.test() function provides a variety of information, including the t-statistic, degrees of freedom, and the p-value. You can interpret the p-value to determine if there is a significant difference between the two groups.

R
# Extract p-value
p_value <- result$p.value

# Check for significance
if (p_value < 0.05) {
  cat("The difference between the groups is statistically significant (p < 0.05).\n")
} else {
  cat("There is no significant difference between the groups (p >= 0.05).\n")
}

This is a basic example. In a real-world scenario, you might be working with more complex data, and you may want to explore other aspects of the t-test, such as effect size.